Antibacterial and Pharmacological Evaluation of Fluoroquinolones: A Chemoinformatics Approach
Bibliographic record
Abstract
Fluoroquinolone (FQ) antibiotics are an important class of synthetic antibacterial agents. These are the most extensively used drugs for treating bacterial infections in the field of both human and veterinary medicine. Herein, the antibacterial and pharmacological properties of four fluoroquinolones: lomefloxacin, norfloxacin, ciprofloxacin, and ofloxacin have been studied. The objective of this study was to analyze the antibacterial characteristics of the different fluoroquinolones. Also, the pharmacological properties of the compounds including the Lipinski rule of five, absorption, distribution, metabolism, and excretion, LD50, drug likeliness, and toxicity were evaluated. We found that among all four FQ molecules, ofloxacin showed the highest antibacterial activity through in silico assays with a strong interaction (‒38.52 kJ/mol) with the antibacterial target protein (topoisomerase-II DNA gyrase enzyme). The pharmacological and pharmacokinetic analysis also showed that the compounds ciprofloxacin, ofloxacin, lomefloxacin and norfloxacin have good pharmacological properties. Notably, ofloxacin was found to possess an IGC50 (concentration needed to inhibit 50% growth) value of 0.286 μg/L against the Tetrahymena pyriformis protozoa. It also tested negative for the Ames toxicity test, showing its non-carcinogenic character.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".